1. Field of the Invention
This invention relates to an image processing method and apparatus. This invention particularly relates to an image processing method and apparatus, wherein a specific image portion, such as an abnormal pattern or a high-contrast image portion, which is embedded in an image, is emphasized selectively.
2. Description of the Prior Art
Image processing, such as gradation processing or frequency processing, has heretofore been carried out on an image signal, which represents an image and has been obtained with one of various image obtaining methods, such that a visible image having good image quality can be reproduced and used as an effective tool in, particularly, the accurate and efficient diagnosis of an illness. Particularly, in the field of medical images, such as radiation images of human bodies serving as objects, it is necessary for specialists, such as doctors, to make an accurate diagnosis of an illness or an injury of the patient in accordance with the obtained image. Therefore, it is essential to carry out the image processing in order that a visible image having good image quality can be reproduced and used as an effective tool in the accurate and efficient diagnosis of an illness.
As one of the image processing, frequency emphasis processing has been disclosed in, for example, Japanese Unexamined Patent Publication No. 61(1986)-169971. With the disclosed frequency emphasis processing, an image signal (i.e., an original image signal) Dorg representing the image density value of an original image is converted into an image signal Dproc with Formula (36). EQU Dproc=Dorg+.beta..times.(Dorg-Dus) (36)
wherein .beta. represents the frequency emphasis coefficient, and Dus represents the unsharp mask signal. The unsharp mask signal Dus comprises super-low frequency components obtained by setting a mask, i.e. an unsharp mask, constituted of a picture element matrix, which has a size of N columns.times.N rows (wherein N represents an odd number) and has its center at the picture element represented by the original image signal Dorg, in a two-dimensional array of picture elements. The unsharp mask signal Dus is calculated with, for example, Formula (37). EQU Dus=(.SIGMA.Dorg)/N.sup.2 (37)
wherein .SIGMA.Dorg represents the sum of the image signal values representing the picture elements located within the unsharp mask.
The value of (Dorg-Dus) in the parenthesis of the second term of Formula (36) is obtained by subtracting the unsharp mask signal, which represents the super-low frequency components, from the original image signal. Therefore, comparatively high frequency components can be extracted selectively by subtracting the super-low frequency components from the original image signal. The comparatively high frequency components are then multiplied by the frequency emphasis coefficient .beta., and the obtained product is added to the original image signal. In this manner, the comparatively high frequency components can be emphasized.
Also, iris filter processing (hereinbelow often referred to as the operation of the iris filter) has heretofore been known as the operation processing for selectively extracting only a specific image portion, such as an abnormal pattern, from an image. [Reference should be made to "Detection of Tumor Patterns in DR Images (Iris Filter)," Obata, et al., Collected Papers of The Institute of Electronics and Communication Engineers of Japan, D-II, Vol. J75-D-II, No. 3, pp. 663-670, Mar. 1992.] The iris filter processing has been studied as a technique efficient for detecting, particularly, a tumor pattern, which is one of characteristic forms of mammary cancers. However, the image to be processed with the iris filter is not limited to the tumor pattern in a mammogram, and the iris filter processing is applicable to any kind of image having the characteristics such that the gradients of the image signal representing the image are centralized.
How the processing for detecting the image portion with the iris filter is carried out will be described hereinbelow by taking the processing for the detection of the tumor pattern as an example.
It has been known that, for example, in a radiation image recorded on a negative X-ray film (i.e., an image yielding an image signal of a high signal level for a high image density), the density values of a tumor pattern are slightly smaller than the density values of the surrounding image areas. The density values of the tumor pattern are distributed such that the density value becomes smaller from the periphery of an approximately circular tumor pattern toward the center point of the tumor pattern. Therefore, in the tumor pattern, gradients of the density values can be found in local areas, and the gradient lines (i.e., gradient vectors) centralize in the directions heading toward the center point of the tumor pattern.
With the iris filter, the gradients of image signal values, which are represented by the density values, are calculated as gradient vectors, the degree of centralization of the gradient vectors is calculated, and a tumor pattern is detected in accordance with the calculated degree of centralization of the gradient vectors. Specifically, the gradient vector at an arbitrary picture element in a tumor pattern is directed to the vicinity of the center point of the tumor pattern. On the other hand, in an elongated pattern, such as a blood vessel pattern, gradient vectors do not centralize upon a specific point. Therefore, the distributions of the directions of the gradient vectors in local areas may be evaluated, and a region, in which the gradient vectors centralize upon a specific point, may be detected. The thus detected region may be taken as a prospective tumor pattern, which is considered as being a tumor pattern. The processing with the iris filter is based on such fundamental concept. Steps of algorithms of the iris filter will be described hereinbelow.
(Step 1) Calculation of Gradient Vectors
For each picture element j among all of the picture elements constituting a given image, the direction .theta. of the gradient vector of the image signal representing the image is calculated with Formula (38). ##EQU1##
As illustrated in FIG. 5, f.sub.1 through f.sub.16 in Formula (38) represent the density values (i.e., the image signal values) corresponding to the picture elements located at the peripheral areas of a mask, which has a size of five picture elements (located along the column direction of the picture element array).times. five picture elements (located along the row direction of the picture element array) and which has its center at the picture element j.
(Step 2) Calculation of the Degree of Centralization of Gradient Vectors
Thereafter, for each picture element among all of the picture elements constituting the given image, the picture element is taken as a picture element of interest, and the degree of centralization C of the gradient vectors with respect to the picture element of interest is calculated with Formula (39). ##EQU2##
As illustrated in FIG. 6, in Formula (39), N represents the number of the picture elements located in the region inside of a circle, which has its center at the picture element of interest and has a radius R, and .theta.j represents the angle made between the straight line, which connects the picture element of interest and each picture element. j located in the circle, and the gradient vector at the picture element j, which gradient vector has been calculated with Formula (38). Therefore, in cases where the directions of the gradient vectors of the respective picture elements j centralize upon the picture element of interest, the degree of centralization C represented by Formula (39) takes a large value.
The gradient vector of each picture element j, which is located in the vicinity of a tumor pattern, is directed approximately to the center portion of the tumor pattern regardless of the level of the contrast of the tumor pattern. Therefore, it can be regarded that the picture element of interest associated with the degree of centralization C, which takes a large value, is the picture element located at the center portion of the tumor pattern. On the other hand, in a linear pattern, such as a blood vessel pattern, the directions of the gradient vectors are biased to a certain direction, and therefore the value of the degree of centralization C is small. Accordingly, a tumor pattern can be detected by taking each of all picture elements, which constitute the image, as the picture element of interest, calculating the value of the degree of centralization C with respect to the picture element of interest, and rating whether the value of the degree of centralization C is or is not larger than a predetermined threshold value. Specifically, the processing with the iris filter has the features over an ordinary difference filter in that the processing with the iris filter is not apt to be adversely affected by blood vessel patterns, mammary gland patterns, or the like, and can efficiently detect tumor patterns.
In actual processing, such that the detection performance unaffected by the sizes and shapes of tumor patterns may be achieved, it is contrived to adaptively change the size and the shape of the filter. FIG. 7 shows an example of the filter. The filter is different from the filter shown in FIG. 6. With the filter of FIG. 7, the degree of centralization is rated only with the picture elements, which are located along radial lines extending radially from a picture element of interest in M kinds of directions at 2.pi./M degree intervals. (In FIG. 7, by way of example, 32 directions at 11.25 degree intervals are shown.)
In cases where the picture element of interest has the coordinates (k, 1), the coordinates ([x], [y]) of the picture element, which is located along an i'th radial line and is the n'th picture element as counted from the picture element of interest, are given by Formulas (40) and (41). EQU x=k+n cos {2.pi.(i-1)/M} (40) EQU y=l+n sin {2.pi.(i-1)/M} (41)
wherein [x] represents the maximum integer, which does not exceed x, and [y] represents the maximum integer, which does not exceed y.
Also, for each of the radial lines, the output value obtained for the picture elements ranging from the picture element of interest to a picture element, which is located along the radial line and at which the maximum degree of centralization is obtained, is taken as the degree of centralization with respect to the direction of the radial line. The mean value of the degrees of centralization, which have been obtained for all of the radial lines, is then calculated. The mean value of the degrees of centralization having thus been calculated is taken as the degree of centralization C of the gradient vector group with respect to the picture element of interest.
Specifically, the degree of centralization Ci(n), which is obtained for the picture elements ranging from the picture element of interest to the n'th picture element located along the i'th radial line, is calculated with Formula (42). ##EQU3## wherein Rmin and Rmax respectively represent the minimum value and the maximum value having been set for the radius of the tumor pattern, which is to be detected.
The calculation of the degree of centralization Ci(n) may be carried out by using Formula (42') in lieu of Formula (42). ##EQU4##
Specifically, with Formula (42'), the degree of centralization Ci(n) is obtained for the picture elements, which are located along the i'th radial line and fall within the range from an Rmin'th picture element, that corresponds to the minimum value Rmin, as counted from the picture element of interest, to an n'th picture element, that falls within the range from the Rmin'th picture element to an Rmax'th picture element corresponding to the maximum value Rmax, as counted from the picture element of interest.
Thereafter, the degree of centralization C of the gradient vector group is calculated with Formulas (43) and (44). ##EQU5##
Formula (43) represents the maximum value of the degree of centralization Ci(n) obtained for each of the radial lines with Formula (42) or (42'). Therefore, the region from the picture element of interest to the picture element associated with the degree of centralization Ci(n), which takes the maximum value, may be considered as being the region of the prospective tumor pattern. By the detection of such regions for all of the radial lines with Formula (43), it is possible to judge the shape of the peripheral edge of the region, which may be regarded as the prospective tumor pattern.
With Formula (43), the maximum values of the degrees of centralization within the aforesaid regions are calculated for all directions of the radial lines. Thereafter, with Formula (44), the mean value of the maximum values of the degrees of centralization within the aforesaid regions, which maximum values have been given by Formula (43) for all directions of the radial lines, is calculated. The calculated mean value is compared with a predetermined threshold value T. From the results of the comparison, a judgment is made as to whether there is or is not a probability that the region having its center at the picture element of interest will be the abnormal pattern.
The region, in which the degree of centralization C of the gradient vector group with Formula (44) is rated, is similar to the iris of the human's eye, which expands or contracts in accordance with the brightness of the external field. The size and the shape of the region is changed adaptively in accordance with the distribution of the gradient vectors. Therefore, the filter used is referred to as the iris filter.
(Step 3) Rating of the Shape and Form of the Tumor Pattern
In general, patterns of malignant tumors have the characteristics of the shapes and forms described below.
1) The side edges are irregular.
2) The shape is close to an ellipse.
3) The region inside of the pattern has a convex or concave density distribution.
Therefore, a judgment is made as to the shape and form by considering these characteristics such that patterns of normal tissues may be eliminated from the detected prospective pattern, and such that only the pattern considered as being a tumor pattern, can be detected. The characteristic measures used in making the judgment include the spreadness, the elongation, the roughness of side edges, the circularity, and the degree of convexity or concavity (i.e., the entropy) of the density distribution in the region inside of the pattern.
For example, the circularity may be employed as the characteristic measure for the shape judgment. In such cases, when the degrees of centralization are binarized, the distribution of the binarized degrees of centralization corresponding to the tumor pattern ordinarily takes a shape close to a circle. The diameter of the circle having the same area as the area of the region obtained from the binary conversion is represented by Le. Also, the lengths of the longitudinal side and the lateral side of a square, which has the minimum area capable of accommodating the region, are respectively represented by a and b. In such cases, the circularity dcirc is defined by Formula (45). EQU d.sub.circ =Le/(a+b) (45)
wherein Le=2(S/.pi.).sup.1/2 PA1 i) carrying out an operation of an iris filter on an original image signal, which represents an image, the degree of centralization of gradients of the original image signal with respect to a picture element being thereby calculated, each of picture elements constituting the image being taken as the picture element, PA1 ii) detecting an image portion, which is associated with a high degree of centralization, in the image in accordance with the calculated degree of centralization, and PA1 iii) selectively carrying out image emphasis processing on the detected image portion. PA1 i) carrying out an operation of an iris filter on an original image signal Dorg, which represents an image, the degree of centralization of gradients of the original image signal Dorg with respect to a picture element being thereby calculated, each of picture elements constituting the image being taken as the picture element, PA1 ii) detecting an image portion, which is associated with a high degree of centralization, in the image in accordance with the calculated degree of centralization, PA1 iii) obtaining an iris filter signal Giris, which represents whether each of the picture elements constituting the image is or is not the one corresponding to the image portion, PA1 iv) calculating an unsharp mask signal Dus, which corresponds to super-low frequency, from the original image signal Dorg, and PA1 v) carrying out an operation with Formula (1) EQU Dproc=Dorg+.beta.(Giris).times.(Dorg-Dus) (1) PA1 i) carrying out a morphology operation on an original image signal Dorg, which represents an image, by using a multiply structure element Bi and a scale factor .lambda., a morphology signal Dmor being thereby obtained, the morphology signal Dmor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi, and PA1 ii) carrying out image emphasis processing on the original image signal Dorg and in accordance with the morphology signal Dmor. PA1 (i) In cases where the image emphasis processing is to be carried out for an image portion (for example, a calcified pattern represented by the high luminance-high signal level type of image signal), in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas (i.e., the image portion is convex) and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi, as illustrated in FIG. 15A, the function .beta.(Dorg) should preferably be set to be a function monotonously increasing with respect to Dorg. Also, (ii) in cases where the image emphasis processing is to be carried out for an image portion (for example, a calcified pattern represented by the high density-high signal level type of image signal), in which the value of the original image signal Dorg is smaller than the image signal values representing the surrounding image areas (i.e., the image portion is concave) and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi, as illustrated in FIG. 15B, the function .beta.(Dorg) should preferably be set to be a function monotonously decreasing with respect to Dorg. PA1 i) carrying out a morphology operation on an original image signal Dorg, which represents an image, by using a multiply structure element Bi and a scale factor .lambda., a morphology signal Dmor being thereby obtained, the morphology signal Dmor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi, PA1 ii) calculating an unsharp mask signal Dus, which corresponds to super-low frequency, from the original image signal Dorg, and PA1 iii) carrying out an operation with Formula (9) EQU Dproc=Dorg+.beta.(Dmor).times.f(Dorg-Dus) (9) PA1 i) calculating an unsharp mask signal Sus, which corresponds to a predetermined frequency, from an original image signal Sorg, which represents an image, PA1 ii) carrying out a morphology operation on a difference signal Ssp, which represents the difference between the unsharp mask signal Sus and the original image signal Sorg, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing a characteristic output with respect to an image portion, at which the difference signal Ssp fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the difference signal Ssp is sharp, and PA1 iii) carrying out image emphasis processing on the difference signal Ssp and in accordance with the morphology signal Smor such that the image portion may be emphasized. PA1 i) an unsharp mask signal operation means for calculating an unsharp mask signal Sus, which corresponds to a predetermined frequency, from an original image signal Sorg, which represents an image, PA1 ii) a morphology signal operation means for carrying out a morphology operation on a difference signal Ssp, which represents the difference between the unsharp mask signal Sus and the original image signal Sorg, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing a characteristic output with respect to an image portion, at which the difference signal Ssp fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the difference signal Ssp is sharp, and PA1 iii) an image emphasis means for carrying out image emphasis processing on the difference signal Ssp and in accordance with the morphology signal Smor such that the image portion may be emphasized. PA1 i) carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing a characteristic output with respect to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, and PA1 iii) carrying out image emphasis processing on the frequency components, which fall within at least a single frequency band and are among the frequency components S.sub.n falling within the plurality of the different frequency bands, and in accordance with the morphology signal Smor, a processed image signal Sproc being thereby obtained. PA1 i) carrying out morphology operations on an original image signal Sorg, which represents an image, by using a plurality of kinds of structure elements Bi.sub.n, which have different sizes and correspond respectively to a plurality of different frequency bands, and a scale factor .lambda., a plurality of morphology signals Smor.sub.n being thereby obtained, each of the morphology signals Smor.sub.n representing a characteristic output with respect to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the corresponding one of the structure elements Bi.sub.n, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) dividing the original image signal Sorg into frequency components S.sub.n falling within the plurality of the different frequency bands, which respectively correspond to the sizes of the plurality of kinds of the structure elements Bi.sub.n, and PA1 iii) carrying out image emphasis processing on the frequency components, which fall within at least a single frequency band and are among the frequency components S.sub.n falling within the plurality of the different frequency bands, and in accordance with the corresponding one of the morphology signals Smor.sub.n, which has been obtained from the morphology operation carried out with one of the structure elements Bi.sub.n, the one of the structure elements Bi.sub.n having the size corresponding to the at least single frequency band, within which the frequency components subjected to the image emphasis processing fall, a processed image signal Sproc being thereby obtained. PA1 i) a morphology signal operation means for carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing a characteristic output with respect to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) a frequency band dividing means for dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, and PA1 iii) an image emphasis means for carrying out image emphasis processing on the frequency components, which fall within at least a single frequency band and are among the frequency components S.sub.n falling within the plurality of the different frequency bands, and in accordance with the morphology signal Smor, a processed image signal Sproc being thereby obtained. PA1 i) a morphology signal operation means for carrying out morphology operations on an original image signal Sorg, which represents an image, by using a plurality of kinds of structure elements Bi.sub.n, which have different sizes and correspond respectively to a plurality of different frequency bands, and a scale factor .lambda., a plurality of morphology signals Smor.sub.n being thereby obtained, each of the morphology signals Smor.sub.n representing a characteristic output with respect to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the corresponding one of the structure elements Bi.sub.n, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) a frequency band dividing means for dividing the original image signal Sorg into frequency components S.sub.n falling within the plurality of the different frequency bands, which respectively correspond to the sizes of the plurality of kinds of the structure elements Bi.sub.n, and PA1 iii) an image emphasis means for carrying out image emphasis processing on the frequency components, which fall within at least a single frequency band and are among the frequency components S.sub.n falling within the plurality of the different frequency bands, and in accordance with the corresponding one of the morphology signals Smor.sub.n, which has been obtained from the morphology operation carried out with one of the structure elements Bi.sub.n, the one of the structure elements Bi.sub.n having the size corresponding to the at least single frequency band, within which the frequency components subjected to the image emphasis processing fall, a processed image signal Sproc being thereby obtained. PA1 i) carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) carrying out first image emphasis processing on the original image signal Sorg and in accordance with the morphology signal Smor such that the image portion may be emphasized, a first processed image signal S' being thereby obtained, and PA1 iii) carrying out second image emphasis processing on the first processed image signal S' and in accordance with the first processed image signal S' such that an image portion, which corresponds to a desired frequency band among the frequency bands of the first processed image signal S', may be emphasized. PA1 i) carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) calculating an unsharp mask signal Sus, which corresponds to a first predetermined frequency, from the original image signal Sorg, PA1 iii) carrying out a first image emphasis processing with Formula (24) EQU S'=Sorg+.alpha.m(Smor).times.(Sorg-Sus) (24) PA1 iv) calculating an unsharp mask signal S'us, which corresponds to a second predetermined frequency, from the first processed image signal S', and PA1 v) carrying out a second image emphasis processing with Formula (25) EQU Sproc=S'+.beta.(S').times.(S'-S'us) (25) PA1 v') carrying out a second image emphasis processing with Formula (26) EQU Sproc=S'+.beta.(S'us).times.(S'-S'us) (26) PA1 i) carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, PA1 iii) carrying out a first image emphasis processing with Formula (27) EQU S'=Sorg+.SIGMA.{.alpha.m.sub.n (Smor).times.S.sub.n } (27) PA1 iv) carrying out a second image emphasis processing with Formula (25) shown above on the first processed image signal S' by using an emphasis coefficient .beta.(S'), which is in accordance with the first processed image signal S', a second processed image signal Sproc being thereby obtained, or PA1 iv') carrying out a second image emphasis processing with Formula (26) shown above on the first processed image signal S' by using an emphasis coefficient .beta.(S'us), which is in accordance with an unsharp mask signal S'us having been calculated from the first processed image signal S', a second processed image signal Sproc being thereby obtained. PA1 i) carrying out morphology operations on an original image signal Sorg, which represents an image, by using a plurality of kinds of structure elements Bi.sub.n, which have different sizes and/or different shapes, and a scale factor .lambda., a plurality of morphology signals Smor.sub.n being thereby obtained, each of the morphology signals Smor.sub.n representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the corresponding one of the structure elements Bi.sub.n, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, PA1 iii) carrying out a first image emphasis processing with Formula (28) EQU S'=Sorg+.SIGMA.{.alpha.m(Smor.sub.n).times.S.sub.n } (28) PA1 iv) calculating an unsharp mask signal S'us, which corresponds to a predetermined frequency, from the first processed image signal S', and PA1 v) carrying out a second image emphasis processing with Formula (25) shown above on the first processed image signal S' by using an emphasis coefficient .beta.(S'), which is in accordance with the first processed image signal S', a second processed image signal Sproc being thereby obtained, or PA1 v') carrying out a second image emphasis processing with Formula (26) shown above on the first processed image signal S' by using an emphasis coefficient .beta.(S'us), which is in accordance with the unsharp mask signal S'us having been calculated from the first processed image signal S', a second processed image signal Sproc being thereby obtained. PA1 i) carrying out morphology operations on an original image signal Sorg, which represents an image, by using a plurality of kinds of structure elements Bi.sub.n, which have different sizes and/or different shapes, and a scale factor .lambda., a plurality of morphology signals Smor.sub.n being thereby obtained, each of the morphology signals Smor.sub.n representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the corresponding one of the structure elements Bi.sub.n, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, PA1 iii) carrying out a first image emphasis processing with Formula (29) EQU S'=Sorg+.SIGMA.{.alpha.m.sub.n (Smor.sub.n).times.S.sub.n }(29) PA1 iv) carrying out a second image emphasis processing with Formula (25) shown above on the first processed image signal S' by using an emphasis coefficient .beta.(S'), which is in accordance with the first processed image signal S', a second processed image signal Sproc being thereby obtained, or PA1 iv') carrying out a second image emphasis processing with Formula (26) shown above on the first processed image signal S' by using an emphasis coefficient .beta.(S'us), which is in accordance with an unsharp mask signal S'us having been calculated from the first processed image signal S', a second processed image signal Sproc being thereby obtained. PA1 i) a morphology signal operation means for carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) a first image emphasis means for carrying out first image emphasis processing on the original image signal Sorg and in accordance with the morphology signal Smor such that the image portion may be emphasized, a first processed image signal S' being thereby obtained, and PA1 iii) a second image emphasis means for carrying out second image emphasis processing on the first processed image signal S' and in accordance with the first processed image signal S' such that an image portion, which corresponds to a desired frequency band among the frequency bands of the first processed image signal S', may be emphasized. PA1 i) a morphology signal operation means for carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) a first unsharp mask signal operation means for calculating an unsharp mask signal Sus, which corresponds to a first predetermined frequency, from the original image signal Sorg, PA1 iii) a first conversion table for receiving the morphology signal Smor and feeding out an emphasis coefficient .alpha.m(Smor), which is in accordance with the morphology signal Smor, PA1 iv) a first image emphasis means for carrying out a first image emphasis processing with Formula (24) shown above on the original image signal Sorg by using the emphasis coefficient .alpha.m(Smor), which is received from the first conversion table, a first processed image signal S' being thereby obtained, PA1 v) a second unsharp mask signal operation means for calculating an unsharp mask signal S'us, which corresponds to a second predetermined frequency, from the first processed image signal S', and PA1 vi) a second conversion table for receiving the first processed image signal S' and feeding out an emphasis coefficient .beta.(S'), which is in accordance with the first processed image signal S', and a second image emphasis means for carrying out a second image emphasis processing with Formula (25) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained, or PA1 vi') a second conversion table for receiving the unsharp mask signal S'us and feeding out an emphasis coefficient .beta.(S'us), which is in accordance with the unsharp mask signal S'us, and a second image emphasis means for carrying out a second image emphasis processing with Formula P8'4 26) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'us), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained. PA1 i) a morphology signal operation means for carrying out a morphology operation on an original image signal Sorg, which represents an image, by using a structure element Bi and a scale factor .lambda., a morphology signal Smor being thereby obtained, the morphology signal Smor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the structure element Bi, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) a frequency band dividing means for dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, PA1 iii) a plurality of different first conversion tables for receiving the morphology signal Smor and feeding out a plurality of different emphasis coefficients .alpha.m.sub.n (Smor) corresponding respectively to the different frequency bands, within which the frequency components S.sub.n fall, PA1 iv) a first image emphasis means for carrying out a first image emphasis processing with Formula (27) shown above on the original image signal Sorg by using the plurality of the different emphasis coefficients .alpha.m.sub.n (Smor), which are received from the plurality of the different first conversion tables, a first processed image signal S' being thereby obtained, PA1 v) an unsharp mask signal operation means for calculating an unsharp mask signal S'us, which corresponds to a predetermined frequency, from the first processed image signal S', and PA1 vi) a second conversion table for receiving the first processed image signal S' and feeding out an emphasis coefficient .beta.(S'), which is in accordance with the first processed image signal S', and a second image emphasis means for carrying out a second image emphasis processing with Formula (25) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained, or PA1 vi') a second conversion table for receiving the unsharp mask signal S'us and feeding out an emphasis coefficient .beta.(S'us), which is in accordance with the unsharp mask signal S'us, and a second image emphasis means for carrying out a second image emphasis processing with Formula (26) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'us), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained. PA1 i) a plurality of morphology signal operation means for carrying out morphology operations on an original image signal Sorg, which represents an image, by using a plurality of kinds of structure elements Bi.sub.n, which have different sizes and/or different shapes, and a scale factor .lambda., a plurality of morphology signals Smor.sub.n being thereby obtained, each of the morphology signals Smor.sub.n representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the corresponding one of the structure elements Bi.sub.n, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) a frequency band dividing means for dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, PA1 iii) a first conversion table for receiving the morphology signal Smor.sub.n and feeding out a plurality of emphasis coefficients .alpha.m(Smor.sub.n), each of which is in accordance with one of the morphology signals Smor.sub.n, PA1 iv) a first image emphasis means for carrying out a first image emphasis processing with Formula (28) shown above on the original image signal Sorg by using the emphasis coefficients .alpha.m(Smor.sub.n), which are received from the first conversion table, a first processed image signal S' being thereby obtained, PA1 v) an unsharp mask signal operation means for calculating an unsharp mask signal S'us, which corresponds to a predetermined frequency, from the first processed image signal S', and PA1 vi) a second conversion table for receiving the first processed image signal S' and feeding out an emphasis coefficient .beta.(S'), which is in accordance with the first processed image signal S', and a second image emphasis means for carrying out a second image emphasis processing with Formula (25) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained, or PA1 vi') a second conversion table for receiving the unsharp mask signal S'us and feeding out an emphasis coefficient .beta.(S'us), which is in accordance with the unsharp mask signal S'us, and a second image emphasis means for carrying out a second image emphasis processing with Formula (26) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'us), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained. PA1 i) a plurality of morphology signal operation means for carrying out morphology operations on an original image signal Sorg, which represents an image, by using a plurality of kinds of structure elements Bi.sub.n, which have different sizes and/or different shapes, and a scale factor .lambda., a plurality of morphology signals Smor.sub.n being thereby obtained, each of the morphology signals Smor.sub.n representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Sorg fluctuates in a spatially narrower range than the corresponding one of the structure elements Bi.sub.n, and/or an image portion, at which a change in the original image signal Sorg is sharp, PA1 ii) a frequency band dividing means for dividing the original image signal Sorg into frequency components S.sub.n falling within a plurality of different frequency bands, PA1 iii) a plurality of different first conversion tables for receiving the morphology signals Smor.sub.n and feeding out a plurality of different emphasis coefficients .alpha.m.sub.n (Smor.sub.n) corresponding respectively to the different frequency bands, within which the frequency components S.sub.n fall, the different emphasis coefficients being respectively in accordance with the morphology signals Smor.sub.n, PA1 iv) a first image emphasis means for carrying out a first image emphasis processing with Formula (29) shown above on the original image signal Sorg by using the plurality of the different emphasis coefficients .alpha.m.sub.n (Smor.sub.n), which are received from the plurality of the different first conversion tables, a first processed image signal S' being thereby obtained, PA1 v) an unsharp mask signal operation means for calculating an unsharp mask signal S'us, which corresponds to a predetermined frequency, from the first processed image signal S', and PA1 vi) a second conversion table for receiving the first processed image signal S' and feeding out an emphasis coefficient .beta.(S'), which is in accordance with the first processed image signal S', and a second image emphasis means for carrying out a second image emphasis processing with Formula (25) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained, or PA1 vi') a second conversion table for receiving the unsharp mask signal S'us and feeding out an emphasis coefficient .beta.(S'us), which is in accordance with the unsharp mask signal S'us, and a second image emphasis means for carrying out a second image emphasis processing with Formula (26) shown above on the first processed image signal S' by using the emphasis coefficient .beta.(S'us), which is received from the second conversion table, a second processed image signal Sproc being thereby obtained.
In cases where the value of the circularity is smaller than a predetermined threshold value, it is judged that the region is not a tumor pattern, and the region is not detected as the tumor pattern. In cases where the value of the circularity is not smaller than the predetermined threshold value, it is judged that the region is a tumor pattern, and the region is detected as the tumor pattern.
By carrying out the steps described above, the iris filter can efficiently detect a tumor pattern from a radiation image.
Further, processing based upon the algorithm of morphology (hereinbelow referred to as the morphology operation or the morphology processing) has heretofore been known as the operation processing for selectively extracting only a specific image portion, such as an abnormal pattern, from an image.
The morphology processing has been studied as a technique efficient for detecting, particularly, a small calcified pattern, which is one of characteristic forms of mammary cancers. However, the image to be processed with the morphology processing is not limited to the small calcified pattern in a mammogram, and the morphology processing is applicable to any kind of image, in which the size and the shape of a specific image portion (i.e., an abnormal pattern, or the like) to be detected are known previously.
The morphology processing is carried out by using a multi-scale .lambda. and a structure element (i.e., a mask) B. The morphology processing has the features in that, for example, (1) it is efficient for extracting a calcified pattern itself, (2) it is not affected by complicated background information, and (3) the extracted calcified pattern does not become distorted.
Specifically, the morphology processing is advantageous over ordinary differentiation processing in that it can more accurately detect the geometrical information concerning the size, the shape, and the density distribution of the calcified pattern. How the morphology processing is carried out will be described hereinbelow by taking the detection of a small calcified pattern in a mammogram as an example.